def test_param_values(self):
gcb = GaussianCNNBaseline(env_spec=test_env_spec,
filters=((3, (3, 3)), (6, (3, 3))),
strides=(1, 1),
padding='SAME',
hidden_sizes=(32, ),
adaptive_std=False,
use_trust_region=False)
new_gcb = GaussianCNNBaseline(env_spec=test_env_spec,
filters=((3, (3, 3)), (6, (3, 3))),
strides=(1, 1),
padding='SAME',
hidden_sizes=(32, ),
adaptive_std=False,
use_trust_region=False,
name='GaussianCNNBaseline2')
# Manual change the parameter of GaussianCNNBaseline
with tf.compat.v1.variable_scope('GaussianCNNBaseline', reuse=True):
bias_var = tf.compat.v1.get_variable(
'dist_params/mean_network/hidden_0/bias')
bias_var.load(tf.ones_like(bias_var).eval())
old_param_values = gcb.get_param_values()
new_param_values = new_gcb.get_param_values()
assert not np.array_equal(old_param_values, new_param_values)
new_gcb.set_param_values(old_param_values)
new_param_values = new_gcb.get_param_values()
assert np.array_equal(old_param_values, new_param_values)
def test_param_values(self, obs_dim):
box_env = GarageEnv(DummyBoxEnv(obs_dim=obs_dim))
with mock.patch(('garage.tf.baselines.'
'gaussian_cnn_baseline.'
'GaussianCNNRegressor'),
new=SimpleGaussianCNNRegressor):
gcb = GaussianCNNBaseline(env_spec=box_env.spec)
new_gcb = GaussianCNNBaseline(env_spec=box_env.spec,
name='GaussianCNNBaseline2')
# Manual change the parameter of GaussianCNNBaseline
with tf.compat.v1.variable_scope('GaussianCNNBaseline', reuse=True):
return_var = tf.compat.v1.get_variable(
'SimpleGaussianCNNModel/return_var')
return_var.load(1.0)
old_param_values = gcb.get_param_values()
new_param_values = new_gcb.get_param_values()
assert not np.array_equal(old_param_values, new_param_values)
new_gcb.set_param_values(old_param_values)
new_param_values = new_gcb.get_param_values()
assert np.array_equal(old_param_values, new_param_values)
